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Application and Simulation of Neuromorphic Devices for use in Neural Networks

Wenke, Sam
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635913955071

Abstract Details

2018, MS, University of Cincinnati, Engineering and Applied Science: Computer Engineering.
Software and hardware artificial neural networks have been increasingly used to solve difficult real world problems. Neural networks have shown successful results in both the High Performance Computing and Big Data fields, where computation is often distributed among clustered hardware, such as CPUs and general purpose Graphic Processing Units (GPUs), to form much larger computing systems. The growing field of neuromorphic computing combines aspects of complex biological systems and classical machine learning methods, accelerated by alternative physical platforms. The field exemplifies a complementary class of neural network computing platforms that aim to emulate neural and synaptic dynamics performed by the human brain with solid-state devices designed to mimic the electro-chemical behavior of their counterpart. These systems can be exceedingly compact and low-power compared to current machine learning methods performed on distributed CPUs and GPUs, and solve similar problems. The physical scalability of neuromorphic platforms is promising due to the advent of material and fabrication technology of both volatile and non-volatile memory, such as resistive RAM (ReRAM). In this thesis, a framework for simulating spiking neurons, ReRAM devices used as synapses, and neural network models is demonstrated and analyzed for biologically-inspired neuromorphic computing research and applications. Additionally, a software framework is designed to handle and simulate various dynamics of ReRAM models used as synapses in complex neural network architectures. A collection of software and mathematical modules are defined to model neuromorphic devices and circuit-level dynamics, as well as, provide a pipeline for analyzing both simulated and collected device data for use in training and testing complex neuromorphic circuitry. Finally, an neuromorphic architecture is proposed and evaluated as dense networks of spiking neuron groups connected by synapses to learn from supervised demonstration.
Rashmi Jha, Ph.D. (Committee Chair)
Marc Cahay, Ph.D. (Committee Member)
Ali Minai, Ph.D. (Committee Member)
67 p.
Electrical Engineering
Neuromorphic Computing; Spike Proximity Correlation; Synaptic Device; ReRAM; Leaky Integrate and Fire; Simulation

Recommended Citations

Citations

  • Wenke, S. (2018). Application and Simulation of Neuromorphic Devices for use in Neural Networks [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635913955071

    APA Style (7th edition)

  • Wenke, Sam. Application and Simulation of Neuromorphic Devices for use in Neural Networks. 2018. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635913955071.

    MLA Style (8th edition)

  • Wenke, Sam. "Application and Simulation of Neuromorphic Devices for use in Neural Networks." Master's thesis, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635913955071

    Chicago Manual of Style (17th edition)

ucin1523635913955071
360
© 2018, some rights reserved.Creative Commons License Application and Simulation of Neuromorphic Devices for use in Neural Networks by Sam Wenke is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.